Causal Intervention and Counterfactual Reasoning for Multimodal Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is crucial for autonomous systems navigating human-populated environments. However, existing methods face fundamental challenges including spurious correlations induced by confounding social environments, passive uncertainty modeling that limits prediction diversity, and bias coupling during feature interaction that contaminates trajectory representations. To address these issues, we propose a novel Causal Intervention and Counterfactual Reasoning (CICR) framework that shifts trajectory prediction from associative learning to a causal inference paradigm. Our approach features a hierarchical architecture having three core components: a Multisource Encoder that extracts comprehensive spatio-temporal and social context features; a Causal Intervention Fusion Module that eliminates confounding bias through the front-door criterion and cross-attention mechanisms; and a Counterfactual Reasoning Decoder that proactively generates diverse future trajectories by simulating hypothetical scenarios. Extensive experiments on the ETH/UCY, SDD, and AVD datasets demonstrate superior performance, achieving an average ADE/FDE of 0.17/0.24 on ETH/UCY and 7.13/10.29 on SDD, with particular advantages in long-term prediction and cross-domain generalization.